Book Image

Data Science Algorithms in a Week - Second Edition

By : David Natingga
Book Image

Data Science Algorithms in a Week - Second Edition

By: David Natingga

Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Table of Contents (16 chapters)
Title Page
Packt Upsell
Glossary of Algorithms and Methods in Data Science


In this chapter, we learned that the k-nearest neighbor algorithm is a classification algorithm that assigns the majority class among the k-nearest neighbors to a given data point. The distance between two points is measured by a metric. We covered examples of distances, including the Euclidean distance, Manhattan distance, tangential distance, and cosine distance. We also discussed how experiments with various parameters and cross-validation can help to establish which parameter, k, and which metric should be used.

We also learned that the dimensionality and position of a data point are determined by its qualities. A large number of dimensions can result in low accuracy of the k-NN algorithm. Reducing the dimensions of qualities of lesser importance can increase accuracy. Similarly, to increase accuracy further, distances for each dimension should be scaled according to the importance of the quality of that dimension.

In the next chapter, we will look at the Naive Bayes algorithm, which classifies an element based on probabilistic methods using Bayes' theorem.